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Showing 1 to 15 of 19 results Save | Export
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Rezwanul Parvez; Alysha Tarantino; Griffin Moores – Online Journal of Distance Learning Administration, 2024
Higher education institutions need to be responsible for understanding the characteristics and qualities of learners who decide to take courses with them; online vs. on-campus and what it takes to keep them learning at an institution. Taking heed and modifying structures, communications, and services will help learners and institutions in this…
Descriptors: College Students, Distance Education, Electronic Learning, School Holding Power
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Jessica Herring Watson; Ayanna Perkins; Amanda J. Rockinson-Szapkiw – TechTrends: Linking Research and Practice to Improve Learning, 2024
This predictive correlational study investigated to what extent, if at all, the constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) predicted special educators' use of assistive technology (AT) in virtual and hybrid settings during the COVID-19 pandemic. A survey was distributed to educators (n = 104) across the United…
Descriptors: Special Education Teachers, Technology Uses in Education, Electronic Learning, Blended Learning
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Costa-Mendes, Ricardo; Oliveira, Tiago; Castelli, Mauro; Cruz-Jesus, Frederico – Education and Information Technologies, 2021
This article uses an anonymous 2014-15 school year dataset from the Directorate-General for Statistics of Education and Science (DGEEC) of the Portuguese Ministry of Education as a means to carry out a predictive power comparison between the classic multilinear regression model and a chosen set of machine learning algorithms. A multilinear…
Descriptors: Foreign Countries, High School Students, Grades (Scholastic), Electronic Learning
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Abu Saa, Amjed; Al-Emran, Mostafa; Shaalan, Khaled – Technology, Knowledge and Learning, 2019
Predicting the students' performance has become a challenging task due to the increasing amount of data in educational systems. In keeping with this, identifying the factors affecting the students' performance in higher education, especially by using predictive data mining techniques, is still in short supply. This field of research is usually…
Descriptors: Performance Factors, Data Analysis, Higher Education, Academic Achievement
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Rajabalee, Yousra Banoor; Santally, Mohammad Issack; Rennie, Frank – International Journal of Distance Education Technologies, 2020
This paper reports the findings of a research using marks of students in learning activities of an online module to build a predictive model of performance for the final assessment of the module. The objectives were (1) to compare the performances of students of two cohorts in terms of continuous learning assessment marks and final learning…
Descriptors: Performance Factors, Electronic Learning, Learning Analytics, Learning Activities
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Almeda, Ma. Victoria; Zuech, Joshua; Utz, Chris; Higgins, Greg; Reynolds, Rob; Baker, Ryan S. – Online Learning, 2018
Online education continues to become an increasingly prominent part of higher education, but many students struggle in distance courses. For this reason, there has been considerable interest in predicting which students will succeed in online courses and which will receive poor grades or drop out prior to completion. Effective intervention depends…
Descriptors: Performance Factors, Online Courses, Electronic Learning, Models
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Wladis, Claire; Conway, Katherine M.; Hachey, Alyse C. – Online Learning, 2016
This study explored the interaction between student characteristics and the online environment in predicting course performance and subsequent college persistence among students in a large urban U.S. university system. Multilevel modeling, propensity score matching, and the KHB decomposition method were used. The most consistent pattern observed…
Descriptors: Online Courses, Electronic Learning, Learning Readiness, Student Characteristics
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Kruger-Ross, Matthew J.; Waters, Richard D. – Computers & Education, 2013
Following the trend of increased interest by students to take online courses and by institutions to offer them, scholars have taken many different approaches to understand what makes one student successful in online learning while another may fail. This study proposes that using the situational theory of publics will provide a better understanding…
Descriptors: Virtual Classrooms, Electronic Learning, Online Courses, Theories
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Ting, Choo-Yee; Sam, Yok-Cheng; Wong, Chee-Onn – Computers & Education, 2013
Constructing a computational model of conceptual change for a computer-based scientific inquiry learning environment is difficult due to two challenges: (i) externalizing the variables of conceptual change and its related variables is difficult. In addition, defining the causal dependencies among the variables is also not trivial. Such difficulty…
Descriptors: Concept Formation, Bayesian Statistics, Inquiry, Science Instruction
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Grosch, Michael – Electronic Journal of e-Learning, 2013
The web 2.0 has already penetrated the learning environment of students ubiquitously. This dissemination of online services into tertiary education has led to constant changes in students' learning and study behaviour. Students use services such as Google and Wikipedia most often not only during free time but also for learning. At the same…
Descriptors: Higher Education, Mass Media Use, Postsecondary Education, Technology Uses in Education
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Punnoose, Alfie Chacko – Journal of Information Technology Education: Research, 2012
The purpose of this study was to find some of the predominant factors that determine the intention of students to use eLearning in the future. Since eLearning is not just a technology acceptance decision but also involves cognition, this study extended its search beyond the normal technology acceptance variables into variables that could affect…
Descriptors: Foreign Countries, Intention, Motivation, Personality Traits
Smith, Peter, Ed. – Association Supporting Computer Users in Education, 2015
The Association Supporting Computer Users in Education (ASCUE) is a group of people interested in small college computing issues. It is a blend of people from all over the country who use computers in their teaching, academic support, and administrative support functions. ASCUE has a strong tradition of bringing its members together to pool their…
Descriptors: Workshops, Administrators, Educational Games, Access to Information
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Yen, Cherng-Jyh; Abdous, M'hammed – International Journal of Distance Education Technologies, 2011
The confluence of technology convergence, market forces, and student demand for greater access is reshaping higher education institutions. Indeed, the convergence of technological innovations in hardware, software, and telecommunications, combined with the ubiquity of learning management systems, is reconfiguring and strengthening traditional…
Descriptors: Learner Engagement, Blended Learning, Distance Education, Educational Technology
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Kundi, Ghulam Muhammad; Nawaz, Allah – Turkish Online Journal of Distance Education, 2011
One cannot predict the details of future but one can surely prepare for it. Researchers in eLearning are capitalizing on the user-perceptions as possible predictor of the user-attitudes towards the development, use, problems and prospects of eLearning in their institutions. This application is founded on the psychological fact that a human's…
Descriptors: Electronic Learning, Foreign Countries, Educational Technology, Predictor Variables
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Wieling, M. B.; Hofman, W. H. A. – Computers & Education, 2010
To what extent a blended learning configuration of face-to-face lectures, online on-demand video recordings of the face-to-face lectures and the offering of online quizzes with appropriate feedback has an additional positive impact on the performance of these students compared to the traditional face-to-face course approach? In a between-subjects…
Descriptors: Feedback (Response), Grade Point Average, Predictor Variables, Lecture Method
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